GitHub – hasaneyldrm/exercises-dataset: A comprehensive dataset of 433 fitness exercises. Each entry includes name, category, target muscle group, equipment, instructions, thumbnail image, and animation video.

You can explore the dataset here: https://github.com/hasaneyldrm/exercises-dataset

A fitness dataset usually sounds dry until you open one that’s actually been built for use. This GitHub repository, hasaneyldrm/exercises-dataset, collects 1,324 exercises with the kind of detail that makes real applications easier to build, not just nicer to look at.

Each record includes the exercise name, category, target muscle group, equipment, instructions, thumbnail image, and an animation GIF or video. That combination matters more than it might seem at first. If you’re building a workout app, a coaching tool, a research project, or even a content library for trainers, you don’t want to stitch together half a dozen sources just to explain a dumbbell movement cleanly. You want the data to behave like a well-organized toolbox, where everything has its place and you can get to it fast.

There’s also a practical detail that stands out. About 25% of the exercises require no equipment at all, which makes the dataset useful for home workout products, beginner programs, or mobile-first experiences where simplicity matters. That’s a smart shape for the data. It reflects how people actually train, not just how gyms are laid out on paper.

The repository also includes two ready-to-use HTML tools that run entirely in the browser. No server setup. No extra moving parts. One is an exercise explorer, the other is a guide for integrating the dataset into your own application. That’s the sort of decision that saves time later, especially when you’re trying to validate an idea before you commit to a bigger build.

The examples in the repository cover the classics too, barbell bench press, deadlift, full squat, pull-ups, lateral raises, biceps curls. Familiar movements, but documented in a way that makes them reusable across systems.

One useful comparison is GeoLibre, which takes a similarly practical approach to structured data in a browser-first environment.

This project is for educational and non-commercial use, and it feels aimed at people who want to move from idea to working prototype without fighting the data every step of the way. That’s a solid place to start, and honestly, a better one than most.